华南农业大学学报2026，Vol.47Issue(1)：74-85,12.DOI:10.7671/j.issn.1001-411X.202507011
基于深度强化学习的柑橘黄龙病智能动态防控策略
Intelligent dynamic prevention and control strategy of citrus Huanglongbing based on deep reinforcement learning
摘要
Abstract
[Objective]Citrus Huanglongbing(HLB)transmission is influenced by the coupling of multiple dynamic factors.Traditional optimal control methods face the limitations in practical applications due to their high computational complexity and reliance on precise models.To address this problem,this paper proposes an intelligent dynamic prevention and control method for HLB based on the Twin Delayed Deep Deterministic Policy Gradient(TD3)algorithm.[Method]Firstly,based on the transmission dynamics of HLB,a HLB propagation dynamics model of the interaction mechanism between host and vector was established.On this basis,the HLB transmission control dynamic model was discretized to construct a Markov Decision Process environment suitable for deep reinforcement learning.Subsequently,the TD3 algorithm was introduced,and a multi-objective reward function compatible with biological constraints was designed.Finally,an HLB prevention and control strategy was proposed.[Result]Simulation experimental results demonstrated that the proposed dynamic prevention and control strategy for HLB based on TD3 exhibited the significant advantages over traditional algorithms across multiple key performance indicators.Compared to DDPG and PPD,the speed of system state convergence to the disease-free equilibrium point increased by 26.59%and 20.99%respectively,the cumulative control cost reduced by 23.79%and 19.90%respectively,and the peak pesticide usage decreased by about 35.57%.Numerical analysis further showed that timely spraying insecticide during the early stages of HLB outbreak played a critical role in interrupting the transmission chain and preventing large-scale epidemics.Compared with constant control strategies,dynamic control strategies had more advantages in suppressing the spread of diseases and reducing the cost of implementing control measures.[Conclusion]The HLB prevention and control method based on TD3 proposed in this study provides a new perspective for the efficient control of HLB transmission,and demonstrates the potential of deep reinforcement learning methods in agricultural disease prevention and control.关键词
柑橘黄龙病/深度强化学习/双延迟深度确定性策略梯度/最优控制/防控策略Key words
Citrus Huanglongbing/Deep reinforcement learning/Twin delayed deep deterministic policy gradient/Optimal control/Prevention and control strategy分类
农业科技引用本文复制引用
张勇威,骆智聪,邓小玲,兰玉彬..基于深度强化学习的柑橘黄龙病智能动态防控策略[J].华南农业大学学报,2026,47(1):74-85,12.基金项目
广东省重点区域研发计划(2023B0202090001) （2023B0202090001）
国家自然科学基金(32371984,62303122) （32371984,62303122）
